The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Machine learning-driven predictions and interventions for cardiovascular occlusions.

BACKGROUND: Cardiovascular diseases remain a leading cause of global morbidity and mortality, with heart attacks and strokes representing significant health challenges. The accurate, early diagnosis and management of these conditions are paramount in improving patient outcomes. The specific disease, cardiovascular occlusions, has been chosen for the study due to the significant impact it has on public health. Cardiovascular diseases are a leading cause of mortality globally, and occlusions, which are blockages in the blood vessels, are a critical factor contributing to these conditions. OBJECTIVE: By focusing on cardiovascular occlusions, the study aims to leverage machine learning to improve the prediction and management of these events, potentially helping to reduce the incidence of heart attacks, strokes, and other related health issues. The use of machine learning in this context offers the promise of developing more accurate and timely interventions, thus improving patient outcomes. METHODS: We analyze diverse datasets to assess the efficacy of various machine learning algorithms in predicting heart attacks and strokes, comparing their performance to pinpoint the most accurate and reliable models. Additionally, we classify individuals by their predicted risk levels and examine key features that correlate with the incidence of cardiovascular events. The PyCaret machine learning library's Classification Module was key in developing predictive models which were evaluated with stratified cross-validation for reliable performance estimates. RESULTS: Our findings suggest that machine learning can significantly improve the prediction accuracy for heart attacks and strokes, facilitating earlier and more precise interventions. We also discuss the integration of machine learning models into clinical practice, addressing potential challenges and the need for healthcare professionals to interpret and apply these predictions effectively. CONCLUSIONS: The use of machine learning for risk stratification and the identification of modifiable factors may empower preemptive approaches to cardiovascular care, ultimately aiming to reduce the occurrence of life-threatening events and improve long-term patient health trajectories.

Biomechanical Effects of Seizures on Cerebral Dynamics and Brain Stress.

Epilepsy is one of the most common neurological disorders globally, affecting about 50 million people, with nearly 80% of those affected residing in low- and middle-income countries. It is characterized by recurrent seizures that result from abnormal electrical brain activity, with seizures varying widely in manifestation. The exploration of the biomechanical effects that seizures have on brain dynamics and stress levels is relevant for the development of more effective treatments and protective strategies. This study uses a blend of experimental data and computational simulations to assess the brain's physical response during seizures, particularly focusing on the behavior of cerebrospinal fluid and the resulting mechanical stresses on different brain regions. Notable findings show increases in stress, predominantly in the posterior gyri and brainstem, during seizures and an evidence of brain displacement relative to the skull. These observations suggest a dynamic and complex interaction between the brain and skull, with maximum shear stress regions demonstrating the limited yet essential protective role of the CSF. By providing a deeper understanding of the mechanical changes occurring during seizures, this research supports the goal of advancing diagnostic tools, informing more targeted treatment interventions, and guiding the creation of customized therapeutic strategies to enhance neurological care and protect against the adverse effects of seizures.

Pembrolizumab-Induced Colitis and Diarrhea in the Treatment of Sporadic Colorectal Cancer: A Case Report.

Pembrolizumab is a programmed cell death receptor-1 (PD-1) blocking immune checkpoint inhibitor (ICI) that is a mainstay of cancer treatment. Pembrolizumab has a lower incidence of colitis and diarrhea compared to other ICIs. The current study presents the case of a 30-year-old female patient on pembrolizumab with stage IV colon cancer who presented with diarrhea (50 times a day) and symptoms of colitis. A computed tomography scan of the abdomen and pelvis suggested proctitis. Stool studies were negative for enteric pathogens, but stool white blood cell (WBC) was positive, and calprotectin was >10,000 ug/g. A colonoscopy showed pancolitis with small internal hemorrhoids. Histopathology showed cryptitis and crypt abscesses with mild focal architectural distortion, mucosal erosion/ulcer, and focal crypt atrophy from the cecum to the rectum. All ICIs were discontinued, and the patient was initially managed with IV fluids. The patient was subsequently started on methylprednisolone and loperamide after colonoscopy. The number of bowel movements decreased to six per day after the above management. The patient was then switched to oral prednisone and discharged with outpatient follow-up. This case reveals the importance of assessing immune-related adverse effects (irAEs) even though incidence rates associated with a specific ICI might be low.

Comparative Analysis of Machine Learning Models for Image Detection of Colonic Polyps vs. Resected Polyps.

(1) Background: Colon polyps are common protrusions in the colon's lumen, with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine learning image classification models' performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOv8n), in the detection and classification of colon polyps was evaluated using the testing split for each model. The external validity of the test was analyzed using 90 images that were not used to test, train, or validate the model. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models' ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the 'normal' class, 0.97-1.00 for 'polyps', and 0.97-1.00 for 'resected polyps'. While GTM exclusively classified images into these three categories, both YOLOv8n and RF3 were able to detect and specify the location of normal colonic tissue, polyps, and resected polyps, with YOLOv8n and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.

A Case of Disseminated Histoplasmosis Presenting As Adrenal Insufficiency With Granulomatous Hepatitis.

Histoplasmosis is a fungal infection caused by Histoplasma capsulatum. In the United States, histoplasmosis is endemic in the Mississippi River Valley and Ohio. Histoplasmosis is often asymptomatic in immunocompetent individuals, and severe disseminated cases are more often seen in immunosuppressed patients. Disseminated histoplasmosis often affects the reticuloendothelial system, invading specific visceral organs such as the liver, spleen, and pancreas. The current study presents a unique case of disseminated histoplasmosis in a 64-year-old immunocompetent male. The patient's presentation included a 40-lb weight loss over a year, bilateral adrenal nodules, abnormal liver enzymes, and granulomatous hepatitis, which initially raised suspicion of a malignant etiology. An adrenal mass biopsy showed fungal morphology that confirmed an H. capsulatum infection. Further history showed that the patient recently traveled to Bangladesh, which is thought to be a region endemic to histoplasmosis. This case is noteworthy because disseminated histoplasmosis rarely affects immunocompetent individuals, and an infectious etiology for adrenal insufficiency is exceedingly rare, especially in the United States. The treatment regimen included a 14-day induction therapy of IV amphotericin B followed by outpatient itraconazole, leading to symptom resolution. This case highlights the need to consider an infectious etiology for adrenal insufficiency, especially among immunocompetent individuals who may be at risk after traveling to endemic areas.

Plasmodium vivax Presenting With Septic Shock and Disseminated Intravascular Coagulation (DIC): A Case Report.

Malaria has various causative agents that can have a spectrum of disease manifestations, some potentially fatal. Various species have been established as etiologies of malaria, though our understanding of the severity of various species is changing. We present a unique case of Plasmodium vivax malaria that resulted in severe disease, a magnitude rarely seen in previous literature. Our patient was a 35-year-old healthy woman who presented to the emergency department with abdominal pain, nausea, vomiting, and fever. Further workup revealed severe thrombocytopenia with prolonged prothrombin (PT) and partial thromboplastin time (PTT). An initial thick smear failed to detect any Plasmodium species, but a thin smear revealed P. vivax. The patient's hospital stay was complicated by septic shock requiring intensive care unit (ICU) admission. This unique case represents P. vivax as the causative agent of severe malaria even in healthy, immunocompetent patients.

Assessment of Abdominal Constrictor's Forces for Informing Computational Models of Orthostatic Hypotension.

Orthostatic hypotension is defined as a sudden drop in blood pressure upon standing from a sitting or supine position. The prevalence of this condition increases exponentially with age. Nonpharmacological treatments are always the first step in the management of this condition, such as the use of an abdominal constriction belt to optimize the blood volume in the abdomen. A multitude of clinical trials have shown the efficacy of elastic abdominal compression as well as compression using an inflatable bladder; however, there are currently few accessible consumer products that can provide abdominal compression by using an inflatable bladder that ensures the correct amount of pressure is being exerted on the subject. This study serves to quantitatively analyze forces exerted in inflatable abdominal binders, a novel treatment that fits the criterion for a first-line intervention for orthostatic hypotension. Quantitative values aim to indicate both the anatomic regions of the body subjected to the highest pressure by abdominal binding. Quantitative values will also create a model that can correlate the amount of compression on the subject with varying levels of pressure in the inflatable bladder. Inflatable binders of varying levels of inflation are used and localized pressure values are recorded at 5 different vertical points along the abdomen in the midsternal line and midclavicular line, at the locations of the splanchnic veins. These findings indicate both the differences in the compressive force applied through elastic and inflatable binding, as well the regions on the abdomen subject to the highest force load during compression by an abdominal binder. A medical manikin called the iStan Manikin was used to collect data. The pressure values on a manikin were sensed by the JUZO pressure monitor, a special device created for the purpose of measuring the force under compressive garments. The pressure inside the inflatable bladder was extrapolated from a pressure gauge and the pressure was recorded at different degrees of inflation of the belt (mmHG) along two different areas of the abdomen, the midsternal line and the midclavicular line, to discern differences in force exerted on the patient (mmHG). Computational studies on the data from the JUZO pressure monitor as well as the data from the pressure gauge on the inflatable bladder allow us to create a model that can correlate the amount of pressure in the inflatable bladder to the amount of pressure exerted on the belt, thus making sure that the patient is not being harmed by the compressive force. The results of our study indicate that there is no significant difference between the pressures exerted on the midsternal and midclavicular lines of the body by the abdominal binder and that no significant difference exists between the external pressure measured by the inflatable belt and the pressure sensed on the human body by the JUZO sensor; however, we were able to extrapolate an equation that can tell the user the amount of pressure that is actually being exerted on them based on the pressure in the inflatable bladder as recorded by the gauge.